This function allows imputing missing values under the assumption that the distribution of complete values has to be Gaussian in each column.
1  impute.igcda(tab, tab.imp, conditions, q=0.95)

tab 
A numeric vector or matrix with observed and missing values. 
tab.imp 
A matrix where the missing values of 
conditions 
A vector of factors indicating the biological condition to which each column (experimental sample) belongs. 
q 
A quantile value (see Details). 
The mean and variance of the Gaussian distribution are determined using a linear regression between the quantiles of the observed values q_{obs}
and the ones of the standard normal distribution q_{N(0,1)}
.
The quantile value is used for determining the minimum of imputed values. This minimum is determined by the minimum observed value in the dataset minus quant_diff(q)
where quant_diff(q)
corresponds to a quantile value of the differences between the maximum and the minimum of the observed values for all the peptides in the condition. As a result, if q
is close to 1, quant_diff(q)
represents an extrem value between the maximum and the minimum of the intensity values in a condition for a peptide.
The numeric input matrix with imputed values. The distribution of the intensity values in each of its columns is supposed to be Gaussian.
Quentin Giai Gianetto <quentin2g@yahoo.fr>
1 2 3 4 5 6 7 8 9  #Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);
#Imputation of missing values with the slsa algorithm
dat.slsa=impute.slsa(tab=res.sim$dat.obs,conditions=res.sim$condition,repbio=res.sim$repbio);
#Imputation of missing values under a Gaussian assumption
dat.gauss=impute.igcda(tab=result$tab.mod, tab.imp=dat.slsa, conditions=res.sim$conditions);

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.